A Proximity-Aware Hierarchical Clustering of Faces

In this paper, we propose an unsupervised face clustering algorithm called "Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clusters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging unconstrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images

Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity

This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT corpus.Comment: Accepted by ICASSP 201

Factors Driving Mobile App Users to Pay for Freemium Services

With the popularity of smart mobile devices, mobile applications (most commonly referred to as an App) have gradually grown up to be a huge commercial market. Therefore, as the variety and download counts of Apps in the application stores of the two biggest operating systems increase, how to make a profit from Apps has become the most concerned issue for developers. Today the freemium strategy is widely observed in mobile App markets. Freemium is a business model by which an App is offered free of charge, but a premium is charged for advanced features. Hence, the purpose of this study is to explore the factors driving mobile App users to pay for freemium services based on value-based adoption model. An online survey was conducted to collect empirical data in order to test the research model. The results of PLS analysis indicate that an App user’s intention to pay is determined by perceived value, a thorough comparison of benefits and sacrifices, and trust of developer. Furthermore, perceived value will be affected by perceived effort and perceived usefulness while the App user has no experience on premium service. Finally, the implications for practitioners and researchers are discussed

Influence on permeability and pore structure of polyolefin fiber reinforced concrete containing slag

The purpose of this study is to assess the mechanical and microscopic properties of concrete containing different dosages of polyolefin fibers and slag through tests of compressive strength, resistivity, water absorption, mercury intrusion porosimetry and scanning electron microscopy. Test results indicate that the specimens containing slag have higher compressive strength, lower absorption, lower resistivity and denser porestructures than the control and specimen made with fibers. The specimens containing slag and polyolefin fiber demonstrated better performances in fiber reinforced concrete. Scanning electron microscopy illustrates that the polyolefin fiber acts to arrest the propagation of internal cracks. Still, there are cracks and weaknesses between fiber and paste that cause harmful ions penetrated easier

Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

Abstract Background Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments. Results ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle. Conclusions The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.Peer Reviewe